Iet Science Measurement & Technology最新文献

Four important quantities in water quality monitoring are temperature, specific electrical conductivity (EC), total dissolved solids (TDS), and pH. In this paper, three sensors for precisely detecting these parameters were designed and fabricated in one structure. Spiral electrodes were made as temperature sensors and circular toothed electrodes were made as EC sensors. The pH sensor comprises two electrodes: the reference electrode (Ag/AgCl) and the working electrode (carbon black/highly porous polyaniline). The response time of the temperature sensor is 13.2 s, and the stability of the sensor is −0.031$���\Omega �{}^{\circ }�C^{�-�1�}�$, and the sensitivity of the sensor is 0.003 $��\Delta �R��R^{�-�1�}�{}^{\circ }�C^{�-�1�}�$. The response time of the pH sensor was reported as 136.2 $�s$ and the sensor's sensitivity was 8.8 $���\mathrm{mV}��p��H^{�-�1�}�$

Electromagnetic devices have gained widespread use in various systems such as renewable energy systems, electrical motors, generators, and transformers. Despite the state-of-the-art modeling techniques, there are differences between the measured thermal behavior of electromagnetic devices and modeled ones. This research aims to bridge this gap by employing a combination of the finite element method and inverse modeling technique via non-collocated sensor configurations. Due to the restricted physical space and economic constraints, only a limited number of sensors can be strategically positioned within a structure. Consequently, the problem of robust and optimal sensor placement holds crucial significance on the accuracy and quality of the collected data influencing the performance, energy efficiency, and the measured thermal behavior of these devices. The objective of optimally locating sensors to acquire temperature data is to minimize the number of sensors and determine the optimal locations for capturing the most sensitive information. In this research, the challenge of robust and optimal sensor placement in the presence of uncertain thermal parameters is addressed using the Gramian-based method, facilitating the reconstruction of thermal properties by capturing the most sensitive temperature data. The experimental and simulation results demonstrate the effectiveness of the proposed approach in optimally selecting and placing thermal sensors and accurately determining the thermal parameters of the electromagnetic devices even in the presence of parameter uncertainties.

Partial discharge (PD) detection is an important technique for monitoring and evaluating the insulation condition of gas-insulated switchgear (GIS) equipment. The joint analysis and diagnosis of multiple signals can effectively improve the sensitivity and reliability of PD detection. In this paper, an integrated ultra-high frequency (UHF) and optical sensor is proposed and designed for PD detection. The effectiveness and sensitivity of the designed sensor are experimentally tested. Furthermore, a 500 kV GIS test platform is built, and PD measurements for different types of defects (metal particle on the insulator surface, floating potential, and protrusion) are carried out based on the integrated sensor. The results show that the integrated sensor can detect discharge signals with a minimum apparent charge below 2 pC and has good detection performance for different types of defects. Due to different propagation and attenuation characteristics, there is no strict correspondence between the amplitude of optical and UHF signals. This means that even if the amplitude of the UHF signal is close, the optical signal amplitude may still differ significantly. Compared to UHF signals, the amplitude distribution of optical signals is more dispersed, resulting in differences in the phase-resolved PD pattern characteristics between optical and UHF signals. Moreover, the effectiveness of the optical method is more easily affected by the sensor and defect position compared to the UHF method, and in some cases, the sensitivity of the optical method is lower than that of the UHF method. The results of this study provide a foundation for a reliable and sensitive PD detection technique in the GIS.

The rapid growth of wearable electromagnetic devices has raised concerns about the potential health effects of electromagnetic fields, particularly due to their interaction with biological tissues. The key parameter for assessing these effects is the specific absorption rate (SAR), which serves as the standard for evaluating energy absorption and associated thermal effects on the human body. However, traditional numerical methods for SAR estimation are computationally expensive, limiting their application to real-time scenarios. This study addresses this limitation by using a deep learning approach to predict the positions of SAR hotspots efficiently and accurately. A convolutional neural network model was developed to predict hotspot locations with minimal computational effort, using tissue distribution and operating frequencies. The dataset includes tissue images augmented with physical properties such as density and permittivity, the latter being frequency dependent, to enhance the model precision. The proposed method demonstrates robust performance of data-driven approaches in predicting SAR hotspots in real time, providing a foundation for safer and more effective deployment of electromagnetic devices, including wearable and medical applications. The source code used in this study is openly available at https://github.com/ShayanDodge/DL-SAR-Hotspots.

Arc faults in low-voltage distribution networks significantly threaten power system safety due to their randomness and concealment. Traditional arc fault detection methods, which rely on time-domain and frequency-domain features, often struggle with accuracy and robustness in variable load environments. To address these challenges, this paper introduces Visibility Graph Convolutional Learning (VisGCL), a novel approach that segments current signals into visibility graphs and employs hierarchical graph convolutional networks for analysis. This method directly learns arc failure modes from the graphical representation of current signals, simplifying the detection process and enhancing both accuracy and robustness. Experimental results demonstrate that the proposed method achieves an accuracy of 98.58 ± 0.14%, with precision, recall, and F1-score reaching 98.05 ± 0.25%, 98.36 ± 0.47%, and 98.16 ± 0.23%, respectively. Extensive experiments validate the effectiveness of VisGCL, confirming its superiority in detecting arc faults under diverse load conditions, and offering a new efficient and reliable solution for arc fault detection in low-voltage distribution networks.

As simulations play a crucial role for the development of modern electrical machines, it is very important to have good material models used in these simulations. Material models are dependent on certain material parameters which often cannot be measured directly and usually require a lot of computational resources to be determined. This paper investigates the application of neural networks and Gaussian processes for the identification of the magnetic permeability in electrical steel sheets. Through the manufacturing process of such steel sheets, different cutting techniques produce different material behaviour in the vicinity of the cutting edge. Therefore, the method requires the generation of datasets dependent on the degradation profile of the cut steel sheets. This is achieved through simulation and the constructed models can be reused without further simulation runs. This paper also uses an ensemble method to mitigate the issue of measurement noise. For the whole training and testing only simulation data is used as actual measurement data is not yet available.

The power wireless sensor network integrates wireless communication and intelligent sensing technology to monitor the operating status of power equipment in real-time, thereby improving the stability and safety of the power system. However, numerous sensor data flows may lead to network congestion and end-to-end delays. In addition, in the power system, monitoring data has different delay constraints and reliability requirements, and it is necessary to divide the flow into different priorities for transmission. Therefore, this paper proposes a scheduling algorithm for power wireless sensor networks that considers service priority. We construct a power wireless sensor network model that includes network topology, queue length and problem definition. Data flow priority and delay constraints are met by introducing queue weight factors and virtual queues. The Lyapunov optimisation method maximises the throughput of the priority classification-based power wireless sensor network. Moreover, the queue stability of the scheduling algorithm is theoretically proved. The simulation results show that the proposed algorithm can ensure the stability of all queues and strictly meet the priority and delay constraints of various data flows in the network.

A method for the optimal design of special transformers is proposed; it is based on machine learning models, which, in turn, are informed by a sequence of magnetic field analyses. The optimal design of a leakage reactance transformer is considered as the case study. The results show that surrogate models amenable to artificial neural networks (ANNs) are able to approximate the dependence of leakage reactance on winding geometry, eventually reducing the computational burden of automated optimal design problems for this class of transformers. Moreover, the deep learning approach based on a Convolutional neural network (CNN) proved to be able to approximate the field distribution in a given region of the domain, knowing the image of the cross-section of the primary winding.

This study presents an innovative approach to identify electrical discharges by proposing an algorithm incorporating fractal geometry concepts. Based on the box-counting method, our algorithm is developed to detect and track the progression of electrical discharges leading to flashover. This is achieved by calculating the fractal dimension of discharge images which are visual representations of electrical activity recorded during experiments on a planar glass insulator model subjected to different levels of contamination. First, the RGB image is transformed into a binary matrix using the NIBLAK binarization algorithm. Subsequently, the acquired matrix is converted into a square matrix, and its fractal dimension is computed for various resolutions. The final fractal dimension of the image is calculated using the least squares method. This latter is applied to the fractal dimensions (FDs) across all resolutions. According to our algorithm, discharge images have FD values ranging from 1.15 to 1.25. FD increases are observed with applied voltage and non-soluble deposit density (NSDD). The density and activity of discharges also increase with FD. Specifically, a discharge is considered “no-arc” if FD is less than 1.2 and “arc” otherwise.

Switchgear can develop insulation defects due to electrical, thermal, and chemical stresses during manufacturing or prolonged operation. Moreover, as voltage levels rise, multiple insulation defects can coexist within the switchgear. Traditional partial discharge (PD) recognition methods often suffer from poor generalisation and low accuracy, limiting their practical applications. This paper proposes a method to identify multiple PD sources by combining generalised S-transform (GST) with the ResNet-18 network. PD tests confirm that the designed monitoring device effectively detects transient earth voltage (TEV) signals from diverse single and mixed insulation defects. Given the non-stationary nature of TEV signals, this paper employs the generalised S-transform (GST) for time–frequency analysis. The findings demonstrate that the GST method offers high time–frequency resolution, significantly improving the feature extraction of various partial discharge sources. Additionally, deep learning algorithms are employed to classify the time–frequency image dataset derived from GST-TEV. The results demonstrate that, compared to traditional manual feature extraction methods, the ResNet-18 network efficiently extracts GST-TEV features from both single and mixed partial discharge sources, achieving a recognition accuracy of 99.41%. This study provides new methods and theoretical support for identifying multiple partial discharge sources in switchgear.